Tableau Sales Dashboard Performance

The following is a guest post contributed by Perceptive Analytics.

Business heads often use KPI tracking dashboards that provide a quick overview of their company’s performance and well-being. A KPI tracking dashboard collects, groups, organizes and visualizes the company’s important metrics either in a horizontal or vertical manner. The dashboard provides a quick overview of business performance and expected growth.

An effective and visually engaging way of presenting the main figures in a dashboard is to build a KPI belt by combining text, visual cues and icons. By using KPI dashboards, organizations can access their success indicators in real time and make better informed decisions that support long-term goals.

What is a KPI?

KPIs (i.e. Key Performance Indicators) are also known as performance metrics, performance ratios or business indicators. A Key Performance Indicator is a measurable value that demonstrates how effectively a company is achieving key business objectives.

A sales tracking dashboard provides a complete visual overview of the company’s sales performance by year, quarter or month. Additional information such as the number of new leads and the value of deals can also be incorporated.

Example of KPIs on a Sales Dashboard:

  • Number of New Customers and Leads
  • Churn Rate (i.e. how many people stop using the product or service)
  • Revenue Growth Rate
  • Comparison to Previous Periods
  • Most Recent Transactions
  • QTD (quarter to date) Sales
  • Profit Rate
  • State Wise Performance
  • Average Revenue for Each Customer

Bringing It All Together with Dashboards and Stories

An essential element of Tableau’s value is delivered via dashboards. Well-designed dashboards are visually engaging and draw in the user to play with the information. Dashboards can facilitate details-on-demand that enable the information consumer to understand what, who, when, where, how and perhaps even why something has changed.

Best Practices to Create a Simple and Effective Dashboard to Observe Sales Performance KPIs

A well-framed KPI dashboard instantly highlights problem areas. The greatest value of a modern business dashboard lies in its ability to provide real-time information about a company’s sales performance. As a result, business leaders, as well as project teams, are able to make informed and goal-oriented decisions, acting on actual data instead of gut feelings. The choice of chart types on a dashboard should highlight KPIs effectively.

Bad Practices Examples in a Sales Dashboard:

  • A sales report displaying 12 months of history for twenty products; 12 × 20 = 240 data points.
    • Multiple data points do not enable the information consumer to effectively discern trends and outliers as easily as a time-series chart comprised of the same information
  • The quality of the data won’t matter if the dashboard takes five minutes to load
  • The dashboard fails to convey important information quickly
  • The pie chart has too many slices, and performing precise comparisons of each product sub-category is difficult
  • The cross-tab at the bottom requires that the user scroll to see all the data

Now, we will focus on the best practices to create an effective dashboard to convey the most important sales information. Tableau is designed to supply the appropriate graphics and chart types by default via the “Show me” option.

I. Choose the Right Chart Types

With respect to sales performance, we can use the following charts to show the avg. sales, profits, losses and other measures.

  • Bar charts to compare numerical data across categories to show sales quantity, sales expense, sales revenue, top products and sales channel etc. This chart represents sales by region.

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  • Line charts to illustrate sales or revenue trends in data over a period of time:

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  • A Highlight table allows us to apply conditional formatting (a color scheme in either a continuous or stepped array of colors from highest to lowest) to a view.

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  • Use Scatter plots or scatter graphs to investigate the relationship between different variables or to observe outliers in data. Example: sales vs profit:

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  • Use Histograms to see the data distribution across groups or to display the shape of the sales distribution:

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Advanced Chart Types:

  • Use Bullet graphs to track progress against a goal, a historical sales performance or other pre-assigned thresholds:

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  • The Dual-line chart (or dual-axis chart), is an extension of the line chart and allows for more than one measure to be represented with two different axis ranges. Example: revenue vs. expense
  • The Pareto chart is the most important chart in a sales analysis. The Pareto principle is also known as 80-20 rule; i.e roughly 80% of the effects come from 20% of the causes.

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When performing a sales analysis, this rule is used for detecting the 80% of total sales derived from 20% of the products.

  • Use Box plots to display the distribution of data through their quartiles and to observe the major data outliers

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Tableau Sales Dashboard

Here is a Tableau dashboard comprised of the aforementioned charts. This interactive dashboard enables the consumer to understand sales information by trend, region, profit and top products.

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II. Use Actions to filter instead of Quick Filters

Using actions in place of Quick Filters provides a number of benefits. First, the dashboard will load more quickly. Using too many Quick Filters or trying to filter a very large dimension set can slow the load time because Tableau must scan the data to build the filters. The more quick filters enabled on the dashboard, the longer it will take the dashboard to load.

III. Build Cascading Dashboard Designs to Improve Load Speed

By creating a series of four-panel, four cascading dashboards the load speed was improved dramatically and the understandability of the information presented was greatly enhanced. The top-level dashboard provided a summary view, but included filter actions in each of the visualizations that allowed the executive to see data for different regions, products, and sales teams.

IV. Remove All Non-Data-Ink

Remove any text, lines, or shading that doesn’t provide actionable information. Remove redundant facts. Eliminate anything that doesn’t help the audience understand the story contained in the data.

V. Create More Descriptive Titles for Each Data Pane

Adding more descriptive data object titles will make it easier for the audience to interpret the dashboard. For example:

  • Bullet Graph—Sales vs. Budget by Product
  • Sparkline—Sales Trend
  • Cross-tab—Summary by Product Type
  • Scatter Plot—Sales vs. Marketing Expense

VI. Ensure That Each Worksheet Object Fits Its Entire View

When possible, change the graphs fit from “Normal” to “Entire View” so that all data can be displayed at once.

VII. Adding Dynamic Title Content

There is an option to use dynamic content and titles within Tableau. Titles can be customized in a dynamic way so that when a filter option is selected, the title and content will change to reflect the selected value. A dynamic title expresses the current content. For example: if the dashboard title is “Sales 2013” and the user has selected year 2014 from the filter, the title will update to “Sales 2014”.

VIII. Trend Lines and Reference Lines

Visualizing granular data sometimes results in random-looking plots. Trend lines help users interpret data by fitting a straight or curved line that best represents the pattern contained within detailed data plots. Reference lines help to compare the actual plot against targets or to create statistical analyses of the deviation contained in the plot; or the range of values based on fixed or calculated numbers.

IX. Using Maps to Improve Insight

Seeing the data displayed on a map can provide new insights. If an internet connection is not available, Tableau allows a change to locally-rendered offline maps. If the data includes geographic information, we can very easily create a map visualization.

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This map represents sales by state. The red color represents negative numbers and the green color represents positive numbers.

X. Developing an Ad Hoc Analysis Environment

Tableau facilitates ad hoc analysis in three ways:

  1. Generating new data with forecasts
  2. Designing flexible views using parameters
  3. Changing or creating designs in Tableau Server

XI. Using Filters Wisely

Filters generally improve performance in Tableau. For example, when using a dimension filter to view only the West region, a query is passed to the underlying data source, resulting in information returned for only that region. We can see the sales performance of the particular region in the dashboard. By reducing the amount of data returned, performance improves.

Enhance Visualizations Using Colors, Labels etc.

I. Using colors:

Color is a vital way of understanding and categorizing what we see. We can use color to tell a story about the data, to categorize, to order and to display quantity. Color helps with distinguishing the dimensions. Bright colors pop at us, and light colors recede into the background. We can use color to focus attention on the most relevant parts of the data visualization. We choose color to highlight some elements over others, and use it to convey a message.

Red is used to denote smaller values, and blue or green is used to denote higher values. Red is often seen as a warning color to show the loss or any negative number whereas blue or green is seen as a positive result to show profit and other positive values.

Without colors:

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With colors:

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II. Using Labels:

Enable labels to call out marks of interest and to make the view more understandable. Data labels enable comprehension of exact data point values. In Tableau, we can turn on mark labels for marks, selected marks, highlighted marks, minimum and maximum values, or only the line ends.

Without labels:

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With labels:

14Using Tableau to enhance KPI values

The user-friendly interface allows non-technical users to quickly and easily create customized dashboards. Tableau can connect to nearly any data repository, from MS Excel to Hadoop clusters. As mentioned above, using colors and labels, we can enhance visualization and enhance KPI values. Here are some additional ways by which we can enhance the values especially with Tableau features.

I. Allow for Interactivity

Playing, exploring, and experimenting with the charts is what keeps users engaged. Interactive dashboards enable the audiences to perform basic analytical tasks such as filtering views, drilling down and examining underlying data – all with little training.

II. Custom Shapes to Show KPIs

Tableau shapes and controls can be found in the marks card to the right of the visualization window. There are plenty of options built into Tableau that can be found in the shape palette.

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Custom shapes are very powerful when telling a story with visualizations in dashboards and reports. We can create unlimited shape combinations to show mark points and create custom formatting. Below is an example that illustrates how we can represent the sales or profit values with a symbolic presentation.

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Here green arrows indicate good sales progress and red arrows indicate a fall in Year over Year Sales by Category

III. Creating Calculated Fields

Calculated fields can be used to create new dimensions such as segments, or new measures such as ratios. There are many reasons to create calculated fields in Tableau. Here are just a few:

  1. Segmentation of data in new ways on the fly
  2. Adding a new dimension or a new measure before making it a permanent field in the underlying data
  3. Filtering out unwanted results for better analyses
  4. Using the power of parameters, putting the choice in the hands of end users
  5. Calculating ratios across many different variables in Tableau, saving valuable database processing and storage resources

IV. Data-Driven Alerts

With version 10.3, Tableau has introduced a very useful feature: Data-Driven Alerts. We may want to use alerts to notify users or to remind that a certain filter is on and want to be alerted somehow if performance is ever higher or lower than expected. Adding alerts to dashboards can help elicit necessary action by the information consumer. This is an example of a data driven alert that we can set while displaying a dashboard or worksheet.

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In a Tableau Server dashboard, we can set up automatic mail notifications to a set of recipients when a certain value reaches a specific threshold.

Summary

For an enterprise, a dashboard is a visual tool to help track, monitor and analyze information about the organization. The aim is to enable better decision making.

A key feature of sales dashboards in Tableau is interactivity. Dashboards are not simply a set of reports on a page; they should tell a story about the business. In order to facilitate the decision-making process, interactivity is an important part of assisting the decision-maker to get to the heart of the analysis as quickly as possible.

Author Bio:

This article was contributed by Perceptive Analytics. Neeru Gupta, Chaitanya Sagar, Prudhvi Sai Ram and Saneesh Veetil contributed to this article.

Perceptive Analytics provides data analytics, data visualization, business intelligence and reporting services to e-commerce, retail, healthcare and pharmaceutical industries. Our client roster includes Fortune 500 and NYSE listed companies in the USA and India.

 

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In this video we will learn to add a “Filters in Use Alert” to a Tableau Dashboard. If you have a dashboard with multiple filters, apply this quick and easy tip to inform your users that filters are in play. This tip builds upon the dashboard that I showcased recently in a previous post: Add a Reset All Filters Button to Your Tableau Dashboard.

I learned this current tip from a presentation given by Tableau Zen Master Ryan Sleeper, so I have to give credit where credit is due.

If you’re interested in Business Intelligence & Tableau subscribe and check out my videos either here on this site or on my Youtube channel.

Add Totals to Stacked Bar Charts in Tableau

 

In this video I demonstrate a couple of methods that will display the total values of your stacked bar charts in Tableau. The first method deals with a dual axis approach while the second method involves individual cell reference lines. Both approaches accomplish the same objective. Hope you enjoy this tip!

If you’re interested in Business Intelligence & Tableau subscribe and check out my videos either here on this site or on my Youtube channel.

Tableau K-Means Clustering Analysis w/ NBA Data

Interact with this visualization on Tableau Public.

In this video we will explore the Tableau K-Means Clustering algorithm. K-Means Clustering is an effective way to segment your data points into groups when those data points have not explicitly been assigned to groups within your population. Analysts can use clustering to assign customers to different groups for marketing campaigns, or to group transaction items together in order to predict credit card fraud.

In this analysis, we’ll take a look at the NBA point guard and center positions. Our aim is to determine if Tableau’s clustering algorithm is smart enough to categorize these two distinct positions based upon a player’s number of assists and blocks per game.

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If you’re interested in Business Intelligence & Tableau subscribe and check out my videos either here on this site or on my Youtube channel.

Create Multiple KPI Donut Charts in Tableau

In honor of National Doughnut Day (June 1st), let’s devour this sweet Tableau tip without worrying about the calories. In this video I we will create a multiple donut chart visualization that will display the sum of profits by a region. Then we’ll use the donuts as a filter for a simple dashboard. Once you finish watching this video you’ll know how to create and use donut charts as a filter to other information on your dashboard.

I know that donuts are not considered best practice, (especially when negative numbers are involved) but they have their uses. Assuming you know that bar charts are a best practice, it never hurts to learn other techniques that add a little “flair” from the boring world of bar charts.

Have you ever looked at a Picasso painting? Obviously Picasso was well versed in painting best practices (understatement) but in some of his art, the people are not rendered in the best practice. Always learn the best practices, but know when to leave them behind and add a little flair! (In no way am I comparing myself to Picasso).

Three-Musicians-By-Pablo-Picasso

Three Musicians – Pablo Picasso

Three Musicians by Picasso is not best practice but it is a work of art!

If you’re interested in Business Intelligence & Tableau subscribe and check out my videos either here on this site or on my Youtube channel.

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If you’re interested in Business Intelligence & Tableau subscribe and check out my videos either here on this site or on my Youtube channel.

 

Basic Statistics in Tableau: Correlation

This is a guest post provided by Juturu Pavan, Prudhvi Sai Ram, Saneesh Veetil and Chaitanya Sagar of Perceptive Analytics.

Statistics in Tableau

Data in the right hands can be extremely powerful and can be a key element in decision making. American statistician, W. Edwards Deming quoted that, “In God we trust. Everyone else, bring data”. We can employ statistical measures to analyze data and make informed decisions. Tableau enables us to calculate multiple types of statistical measures like residuals, correlation, regression, covariance, trend lines and many more.

Today let’s discuss how people misunderstand causation and correlation using Tableau.

Correlation and Causation

Correlation is a statistical measure that describes the magnitude and direction of a relationship between two or more variables.

Causation shows that one event is a result of the occurrence of another event, which demonstrates a causal relationship between the two events. This is also known as cause and effect.

Types of correlation:

  1. 1 → Positive correlation.
  2. -1 → Negative Correlation.
  3. 0 → No correlation.

Why are correlation and causation important?

The objective of analysing data is to identify the extent by which a variable relates to another variable.

Examples of Correlation and Causation

  1. Vending machines and obesity in schools: people gain weight due to junk food. One important source of junk food in schools is vending machines. So if we remove vending machines from schools obesity must reduce, right? But it isn’t true. Research shows that children who move from schools without vending machines to schools with vending machines don’t gain weight. Here we can find a correlation between children who were overweight and eating junk food from vending machines. In actuality, the “causal” point (which is the removal of vending machines from schools) has a negligible effect on obesity.
  2. Ice cream sales and temperature: If we observe ice cream sales and temperature in the summer, we can determine that they are causally related; i.e. there is a strong correlation between them. As temperature increases, ice cream consumption also increases. Understanding correlation and causation allows people to understand data better.

Now let’s explore correlation using Tableau. We are going to use the orders table from the superstore dataset which comes default with Tableau.

Before going further let’s understand how to calculate the correlation coefficient ‘r’.

We can easily understand the above formula by breaking it into pieces.

In Tableau, we can represent the above formula as 1/SIZE() -1 where SIZE is function in Tableau.

We can use WINDOWSUM function for doing this summation in Tableau.

xi is the sum of profit and x-bar is the mean of profit, which is window average of sum of profit, and sx is standard deviation of profit. That means that we need to subtract mean from sum of profit and divide that by standard deviation.

(SUM([Profit])-WINDOW_AVG(SUM([Profit]))) / WINDOW_STDEV(SUM([Profit])))

This is similar to the formula above but we only need to swap profit with sales.

(SUM([Sales])-WINDOW_AVG(SUM([Sales]))) / WINDOW_STDEV(SUM([Sales])))

Now we have to join all these formulae to get the value of the correlation coefficient of r. Be careful while using parenthesis or you may face errors. Here is our final formula to calculate r.

1/(SIZE()-1) * WINDOW_SUM(( (SUM([Profit])-WINDOW_AVG(SUM([Profit]))) / WINDOW_STDEV(SUM([Profit]))) * (SUM([Sales])-WINDOW_AVG(SUM([Sales]))) / WINDOW_STDEV(SUM([Sales])))

Let’s implement this in Tableau to see how it works. Load superstore data into Tableau before getting started.

After loading the superstore excel file into Tableau, examine the data in the orders sheet. You can see that it contains store order details complete with sales and profits. We will use this data to find correlation between profit and sales.

Let’s get our hands dirty by making a visualization. Go to sheet1 to get started. I made a plot between profit and sales per category.

Now in order to find the correlation between profit and sales, we need to use our formula to make a calculated field which serves our purpose.

Now drag and drop our calculated field onto the colors card and make sure to compute using customer name as we are using it for detailing.

Here we can see the strength of the relationship between profits and sales of data per category; the darker the color, th he stronger the correlation.

Next we’ll add trend lines to determine the direction of forecasted sales.

These trend lines help demonstrate which type of correlation (positive, negative or zero correlation) there is in our data. You can explore some more and gain additional insights if you add different variables like region.

From this analysis we can understand how two or more variables are correlated with each other. We begin to understand how each region’s sales and profits are related.

Let’s see how a correlation matrix helps us represent the relationship between multiple variables.

A correlation matrix is used to understand the dependence between multiple variables at same time. Correlation matrices are very helpful in obtaining insights between the same variables or commodities. They are very useful in market basket analysis.

Let’s see how it works in Tableau. Download the “mtcars” dataset from this link. After downloading it, connect it to Tableau and explore the dataset.

The dataset has 35 variables where each row represents one model of car and each column represents an attribute of that car.

Variables present in dataset:

Mpg = Miles/gallon.

Cyl = Number of Cylinders.

Disp = Displacement (cubic inches)

Hp = Gross Horsepower

Drat = Rear axle ratio

Wt = Weight (lb/1000)

Qsec = ¼ mile time

Vs = V/Sec

Am = Transmission (0 = automatic, 1 = manual)

Gear = Number of forward gears

Carb =Number of Carburetors

Let’s use these variables to make our visualization. I made this amazing visualization showing correlation between models by referring to Bore Beran’s blog article, in which he explained how to make this visualization which helps us understand more about using Tableau to understand correlation.

Conclusion

We must keep in mind that if we want to measure the dependence between two variables, correlation is the best way to do it. A correlation value always lies between -1 and 1. The closer the value of the correlation coefficient is to 1, the stronger their relationship. We must remember that correlation is not causation and many people misunderstand this. There are many more relations and insights that can be unlocked from this dataset. Explore more by experimenting with this dataset using Tableau. Practice to be perfect.

Author Bio

This article was contributed by Perceptive Analytics. Juturu Pavan, Prudhvi Sai Ram, Saneesh Veetil and Chaitanya Sagar contributed to this article.

Perceptive Analytics provides Tableau Consulting, data analytics, business intelligence and reporting services to e-commerce, retail, healthcare and pharmaceutical industries. Our client roster includes Fortune 500 and NYSE listed companies in the USA and India.